{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.KNN算法：K最近邻(kNN，k-NearestNeighbor)分类算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](pic/1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](pic/2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.关于K的取值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](pic/3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.K的取法：#\n",
    " 　　常用的方法是从k=1开始，使用检验集估计分类器的误差率。重复该过程，每次K增值1，允许增加一个近邻。选取产生最小误差率的K。\n",
    "\n",
    "　　一般k的取值不超过20，上限是n的开方，随着数据集的增大，K的值也要增大。\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.关于距离的选取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](pic/4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](pic/5.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6.总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](pic/6.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 代码部分："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建简单数据集\n",
    "def creatDataSet():\n",
    "    group=np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])    #已分类数据坐标,4个点 2个类\n",
    "    labels=['A','A','B','B']                               #tag for every dataset\n",
    "    return group,labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[1. , 1.1],\n",
       "        [1. , 1. ],\n",
       "        [0. , 0. ],\n",
       "        [0. , 0.1]]), ['A', 'A', 'B', 'B'])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "creatDataSet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify0(inX,dataSet,labels,k):       #inX 分类目标\n",
    "    #计算inX与每个点的距离\n",
    "    diffmat=dataSet-inX                                \n",
    "    sqdiffmat=diffmat**2\n",
    "    sqdiffmat=np.sum(sqdiffmat,axis=1)\n",
    "    distance=sqdiffmat**0.5\n",
    "    \n",
    "    sorteddistanceindex=distance.argsort()   #根据以上计算的距离排序\n",
    "    ans={}\n",
    "    for i in range(k):                  #对比前K个数据，对类别计数\n",
    "        ans[labels[sorteddistanceindex[i]]] =ans.get(labels[sorteddistanceindex[i]],0)+1  \n",
    "        # vote=labels[sorteddistanceindex[i]]\n",
    "        # ans[vote]=ans.get(vote,0)+1\n",
    "    sortedclasscount=sorted(ans.items(),key=operator.itemgetter(1),reverse=True)  #对ans排序，从大到小,itemgetter决定根据哪个元素排序\n",
    "    return sortedclasscount[0][0]  #返回次数最多的类，注意sorted返回的是一个list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](pic/sorted.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def file2matrix(filename):    #读取数据，分别存储数据行和对应label\n",
    "    fr=open(filename)\n",
    "    arrayOLines=fr.readlines()\n",
    "    numberOfLines=len(arrayOLines)\n",
    "    returnMat=np.zeros((numberOfLines,3))\n",
    "    classLabelVector=[]\n",
    "    index=0\n",
    "    for line in arrayOLines:\n",
    "        line=line.strip()            #去掉头尾空格等标识符\n",
    "        linesplit=line.split('\\t')   #按\\t分割，返回结果为列表[]\n",
    "        returnMat[index,:]=linesplit[0:3]    \n",
    "        classLabelVector.append(linesplit[-1])  #每行数据最后一列为tag\n",
    "        index+=1\n",
    "    return  returnMat,np.array(classLabelVector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "returnMat,label=file2matrix('data/datingTestSet.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pylab import mpl "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_KNN(datingDataMat,datingLabels,n,m):    #对数据可视化\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    mpl.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    kind = list(set(datingLabels))\n",
    "    markers = ['o', '*', '+', 'x', 's', 'p', 'h']\n",
    "    col = ['b', 'r', 'g', 'c', 'y', 'm', 'k']\n",
    "    label_3 = [r'不喜欢', r'一般', r'特别']\n",
    "    for i in range(len(kind)):\n",
    "        xx = datingDataMat[datingLabels == kind[i]]\n",
    "        yy = datingDataMat[datingLabels == kind[i]]\n",
    "        plt.scatter(xx[:, n], yy[:, m], marker=markers[i], c=col[i], label=label_3[i])\n",
    "    plt.xlabel('x')\n",
    "    plt.ylabel('y')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zvm5d8udfu5bzpib6vamJ8wULqJ3ly+P3ra+32o9G6XUy1NXZt5UGAKznNrpqLHqDwZA+7Czpu+8Oprb80UcDZ55Ji5EceSRZ9oDeNaNa/16fKFRywA1khN5gMKSPVFegFMHVUaPcXTPCBXTDDZbrJxFS4QYKGCP0BkMaCWoN45wmKEtaRrWqb7wR+PBDYP9+aqO1Ffj8c6BPH+uYESOsWjn9+iX+RJED5ZNTIvSMsX6Msde7fj+cMbaTMdbQtfVNRZsGbxihMWQcL5a018Cm2G/WrHiruqjIGlCKiij4m6o0ylQMXgESeD16xthBAH4N4BDO+UmMsYsA9OOcP+LleF09ekNwBL2WrsEb4r6vblwNAKiprgFgvgdbli+n2a7LlwOTJ3vbr7AQuPhioLMTiEQor/4vfwF+9ztafrC9HYhGU7P84Lp1NND06wd8/DGVT07zerbprkffAeCbAJ7ten0qgLMYY98BsIpzfnMK2jS4oAqNEXxDVqKuCzt9OvDtb5NA79hhFS7T7Uc5L7QVFQFvvw3cdReJeqqXH8zy8smBCz3n/HMgZkGOlwAsALAXwO8ZYydyzt+Rj2GMXQHgCgCoqqoKuksGDRubNma6CykhWwcw0Z9s7V/WoFsX9qCDgI8+IreLsO7V/Ti3LHmA/OTPPUdlEebPp+N69KCBIcv85+kgHcHYNzjnX3DOOwC8DeAodQfO+WOc8+Gc8+F9+xoXfipomNGAhhkNqKmuQU11DYYdOgzDDh2W6W55wsQVQo7sj5cDmwUFwL59QFMT7SenLaoBUMaAww6zauAUFgKDBpHlbuc/z+YJTgGTjnr0LzPGJgNoAXAOgEfT0KbBBmHJtxygP+6wWJi54prKtv6kFVFDXq0dL9ebmTzZEuaZM6kMsSAaJWG/8UZ6LfYT68dWVZF/XLXc7ZYfVNsNMemw6OcBeA3AnwH8jHP+fhrazFlSbb3moiW/unE1VjeuNpZ9rqMWELObaNTSQtk4ixYBj3bZhSJFsrUV2LyZ3lOzd8rK9Ja7mkZ5771ZP8EpaFJm0XPOa7t+vgbg2FS1Y/BHWH3FYb2uUDBlCvDss8DevfR6+nTgO98BzjiDrHA1UPrww5Ywv/IK+d3b2sgHLx8vZ87060cDg8h8cVo4XBcHyLIJTkGTE0sJ5gO54npQ8dJPp32cPnMS71y5P0lj5+7IJebPB1avtoReCOtPf0qZMU6B0lmzgOuvBy67DPjHP2gClJ0we818Ef79PArQmpmxeYoIzoYNP9eVE66gXK+XPmUKMGyYFVAFKMB60EEkrG4TjUaMAE49lYS5vT24madZPsEpaAKfMJUs+T5hKlcsVS8TgHT7bGzaGBMj8DuBKMiJR1l9r+U88VRO9Ek127ZRn99/n1wv0Sj9PPdcGry8TjSaNIncOCLweu65wJNPJt6vLJjgFDTpnjBlMGQ1OeEmC4MfuaUFuPBC4KabgMsvp2BpaysFWId1DfZe3S12mTOJkuUTnILGWPSGpOi9uDcAoHl2s+0+tUtruy151RIX+BXZZMQ5Z8oRPPUU+ZGLi8my//WvqfxuNiPHFFaupPIEp5wCvPceWePz59N+8ixX3bHyZ2GIU6QBJ4ve+OgNCSH82y0HWtByoCUwf3c6/Obq5LGsjVfkoh9ZxBT69bPSF9evJ7/6hg3A4sVURVIXcxDHqpOYcj1OkQUY143BkSDcGqnIlslKYQ6aoN0VqUTEFPbvp9eiBg1As1WjUeCZZ6zBSk6RBGLr1nz0EV3zoYcCn34aW89GTas0eMIIvSEh3PLW/Qp6Mn7zRAcPP/tnxI+fS35kEVPYtg3o6KDyBR0dQGkpWfN33AE89BCwZQvtL8ccOCervb3dOt+BA5SpU1xM++ZqnCJLMEJv0JJswNKuaFpQQhmU8GZlIDYXufVWWuijrY1ed3TQz2OOofz3NWsoU+b990m829ooSHvBBeR7v/124OqrrfMVFVH65MyZwHXX5U2+e6owQm9ICjtL3q6Wjp2wJjKzdWPTxu4SCX6P9UpOZOhkA/PnAy+8EOuyKS+ngPIPfxjrmhGzXK+/Hti1i3zvr79OWTn799NnIlderWdTVwfcdpsJzPrECL1BixAykVWTqCWvs+yFQAeRMZNoueVEBTys5Z2TZvBg4OabSZBFltCPf0zW9113kSX/4YexA0FzV6bW9Onkwz/4YGDPHuDaa2mx8Pr6+DjF44+Trz8PCpEFiRH6LCDTVqJTiQG/VS7FZCghoOK1TqB1Yu/3Hgw7dFh36mYq7p/6pGFw4K23Yq3vt96i99WSA/v3A4cfDvznPzRLtrAQGDiQ3DennUaiPnNm7CQm3UIjfgKzeZ6iaYTe4AtV8OyEWn0SELn0gpYDLdjYtBG9F/f2JdK68yciwn5dRWEt7xwoTllCXkoKf/3r1v5q8DnZCWR5VJJYh8mjzyCZLsPr1H6yueZqOeSGGQ0YdugwVBRXxOyTaJ/l/H1x/lSSS+WdU4rTYh1qOWC5pIDXksJ2qAuNeK13Y1cKOcQliXUYiz7HSZfQqa4XgZ1FbleNUmfZr25c7WmGbSrw+ySR9y6cRC1jNVXUa0lhGfWpoL4+dqawzj0ThlISAWCEPoNkuoa6l/aDEDj1KUEVe7/Hq+8li1yiwbhibEjWR66SyBwBtwlkukEoD0sS6zCumyzFzY3j5HZJhQtIduVUFFd0u2BUn7XXczXPbkbDjIbuc/kppeBnoKhdWovei3sHfj/ybtWr+fPJAi8spNfptIyFu+joo/WuoSlTSMSnTqXXqnsmF0tJBIyx6LOAdFmRbjnsyaDLohHumEQDmELQdYOJsL6TLW4mn1+4kIxlryGTlrGbu8huYRMxCOVSKYkUYYQ+y/Ca361zYchWvtOxyWBXtyZRq1b45HUTq+xy8mXhV+vby/1wE/GNTRuxu3W3r/6mw92WtRk9ifjIk8GLu0itsQPELmwC5FYpiRRhhD4PUAePIK1W3exUIb5CZIWbx097qqiLc8iljoWQCxeKX+QBwvjoPZCIjxxIfADwEkgV+8g1dDo68jJX3gkj9FmGX4tR/tzu2FT5kJOx5HXnAuInasnI7hq1vr06MNRU13QLuSri6sBXUVyRsGUfJFlVbkEnznaWsZvlnWimjhd3kdjn4ov1C5sYAJhgbEJkewBuY9PGGLeHEIpkAqh26PLtRaBVvNc8uznhtEkRrBXnVDNkRBvJ5LkPO3QYRleN1gpqpr9r8cSUdvzUgLcL1H7xRfI57F4CqStW0Hnnz6d9fve7nF8WMGiMRZ+lJGPFZePEHlWgvcQexGClXo/OsvfzJJTptFY77OIudqSk/4mkUcpWNWNkVc+bR1b1BRckl8PuJZBqgq2uGKH3QVY9Wmtw6l8i4uZlX6cc9CDuizow6GIBfttRxdOp1k+mvut0VObUkugEoxUrqLTwvn3087LLSHCTzdTxEkg1wVZXjNAbPOMmNk6ZM3L2S3R+FB28I+YYgSzqcqBVza7RBWK9lGlwegpQ0eXqp0NwvVryKRkEEkmjnDIFeP55a+GQAwdI8MeOBfr3d87UMaQFI/RdBPHIn2kL389MVyfshETdR01fBBBTyyYRdKIOOAdi/Z5bDBDR+fTnrw466gxenStMN0AFSUbdS25plCpq5ktnJ/38y1+At98GzjuPatwYt0rGMEJvcGXNjjUALEHsvbg3drfuRnlRedy+LQda4iZKyeJfUVyhFU51oHDy4ycyWUodmOTr0e0r90nk25cXlWvTPdNNygcBrz5vOTNHPAUwRksDArSc4KBBtFDIccfRfiZImhHyXugTeQy2s+TT5U91O7+Ta8WLX1sVEtX9AgDlReVont2M3ot7x6VC2g0CXnCaDCX3zS+qG6aAFdgKvY4O3hFzneqTTKq+84w8HXr1ecuZOb/9LYl8JGItI9jWBnzwAXDVVbHplbrUzUzXi890+ynGpFeGjGTSAdVjRZqmqEMjkOvS7G7djQJW0P1ZRXEFRleNRvPs5u66ODq8+NO9DEi6QVd3/SL9sqa6xvZ84nrVuj7yYFPACmKuN5N4uYcpQVf69/nngdpaymWXOXAAWLfO2q+8nHz3auqmn3RON5xKKdsRZPtZSN5b9EE8BqfLn5rok0OiNV3U1aJUNjZt7LbsvT4tuE1McptFq0s9dMsKAmJdQwWsAKOrRsddl84No7qJ7MjWlE3feLFsdZk5AwfSEoJyKQKBcOVwTvVo1q6l19OnA9Om0e+MWe8lUhVT7refCVpBV+XMUvJe6MNCMu4jp2Nrl9Z2V5eUEbNJZReNTuTVoKkc+NSlfXpBN4nISwBZ9EdcmxqMVXEKZmfzhLmk8CKSdpk5w4YBJ51ELhtBSQmJv1hCMBKxgrWFhbSkIOfAv/6VXL140e9+/SzXkRfRzpN69UbouwjCAku1FZeo1aimOwZR02V36+5uv7Xf/jiVGLabNKRm2qzZsSZm0ND589UJWqJd0YYYpHTlFuz6Zfc6iIE2o08Bfi1bu8wcdYHwPn2AnTuB8eOBp5+mtEuAPm9rowVIgMRz7dV+y4uPexHtPKlXb3z0IUFXikAVDrdqjbpjG2bELwGosmbHGqzZscbWPy7Xrwf0y/J58TfLA4RqhaslEUS/ncjV5QFTUpbBb715dWnAWbPofbFA+KBBZCHv3Env19VZIg+QoLa3k8DW1enLHHjxtav9FpSW0kBy4400WO3YYX+uPKhXbyz6HCRRy092W/hFHiAKWEFMqqGXDBvdAGM3wUq23OXJVcK/Lr/WXY86q1RO90y00qUbycw8zoqZ1n4tW7vMHJGaWVdHg4ATPXtSPZyTTwZ+9rP4dE6/biTxFFFURAPUggXAI4/QOYYNAz77TH+uPCihYIQ+C6irA+bMIaOjqgpYuNBaLMcrTiLh5r8W7ztlpMjCPrpqNAArv15NNXTKo1fRLVhix7BDh8XVv1GP9xI4FccFsXhJOkn5wCAs2x/+kHLf6+r8z2K9775YV4oTX3xBP2+9FVi82HITXXedPzfS9dfT04Hw/be1ATfdRL+LwO9nn9HPqVPjz5UHJRSM0GeYujrgiiusxXEaG+k14F/sU4FuopEQSa8+buHPB+hpYHXj6pgA75oda+Ly8dWnBjFYqBk+dq4X2c0jDwqpxksGlLqfGGAzPtAIy/YPfyDRTGRy0/z5ZImLcggq8oQqQTQa6ybyGyD96U+B2bPJTSRq7RxxBNDUBOxWniQ5p4lcIQu2uuFJ6BljkwA8yzn3MEwbAO/W1pw5lsgL9u6l970IvRcrL9HSDTpXSgEr6LaEheUuU8AKYlaNWrNjTYxgC2RXTgfvcE27VD/f3bpbu8ygW2pnB++wrZOjniddlr7XASjlKZyqNT53LgVLnbJW1HTMwYOB228Hrr7a2qewEPjSlyjvvm/f2M8Aa2LVLbdQO37dSBddRAOTvP+iRcD//V98WwA9Mocs2OqG12DsEACvMcYeZYyNSmWH8o0dO/y9nyx+65vLQU5hZcsCowpwB+/oDhZubNrYnZkjJhkJy14Npo6uGo2K4oruQGr7re1ont3cfZyoF6+eV702ndtICKloU306SQfinohFxXsv7t39e9aQyALguolGr79OE6dEbnxbGwnxDTfQI6yOwkKaWNXSQttll9E5vAZIdQFV0Q+ZsjJ6P89gXH2MctqZsRoATwDoBLCQc7406A4NHz6cr1+/PujTpg3VwnZ7LB8wgNw1KtXV9OTqt107q09OL1TTK+2OVVdgEkIpX5Ow2tXPBPKTgFPJgZrqmpiaMsJFIwuh2g9xXnXSFhAr9GIwUtuXj9UNEPJqVfL9UUl04pY6UGWFC+epp2IDm7/+NfnpVctdTmtsbyf3S3ExWf+7dgG//73lM5eR8+gBcqMAJM6ffw4sWULxgY8+okDqd78LfPwxBUidXEnr1tEg1a+ftT/nNEi9+iqd52c/A846i54cQlhzhzG2gXOuvTBPFj1jbBJj7BkAtwK4A8CpAGY67N+PMfZ61++FjLHnGWN/Yoxd5r/74WbhQr3RsXBhsO0IEZVdFl4te6faNaqvWaRJqume7be2g9/Gu33tBawgblCQ/f7C4pXRTdoS+4tBoeVAS7ewi9WtRleNjqhec8sAACAASURBVOm/yN4pLypPW3qlej+aZzfHpZ1mBcIynj2bBFlY4MJyFymKTtb/Qw/RZ8Kil1HFv7OTJjh9/jm9/va3SeQB4Ac/oJIJ113nLswjRlhB1H79aP8RI0jUP/gAuPtuyz0UQpF3w5NFzxibC+BXnPMPpfeGcs43afY9CMCvARzCOT+JMXY9gF6c87mMsRcBfJNz/oVdW+my6NVMl/PPp6fPZDJfZPz4UYPIulHbtbPGBbrFtlWLUraQBbIw6tIi5c/U99WgrtwHXeE0GdmFI85ltyiJfIwQc/nJAkBcoNcu6Ctfp4rfpzf5GDnG4fXJIS0Iy/gPf6A/whNPBLZtiw0kFReTX/yii/TWP0BPBt/8pt6qF4iBwEmDevWi3HwvPvWQFyZzw8mi9xSM5ZzP1bwXJ/JddAD4JoBnu17XApjd9fsfAQwH8JqXdlOFLtPlkUeszxvPqMUlvwdmNFBcaPNNDSntz9Sp6c2wEQFVJ9RsG10hL53AqoLvtVaMG2KgEhk7gCWI4qda0kAX5BWWfSL17P3iFBgXfQDsawllBDUgu2lTfAbNgQPAM88Aa9bQxKS9e+kxVMyOnTKFngycRB4ggT/sMODTT2n5QR1+AqeJLkKeB/jy0fs6MWMNnPNaxtgfAFzEOW9hjF0B4HPO+W+Ufa8AcAUAVFVVndyoc1oHiJ1fvJsZtd2/RiLAr85ssBVinTUOeLPQU2HJB+Gjl/dXLWzdhCU7y1i2VFXLXvdkAKA7zVLnwxbI51OPVZ9e1OqT6qCjPknYndsJv9lMXo/PCNu2Weu8yjNZZRgDhgyh7JrnngOWLqXg6cyZ5BbZtg04+2xKb9QVOSsro8VInn4aOOoo2p/z2NTLSITq5IwbBzz5pHOfneIFISpM5kbSPvok2Q2gtOv3cl2bnPPHOOfDOefD+/btm/IO2Wa0zKilbcDq7q2zajUuf13vzxZPBo2N9PfZ2Eh/79/6Vux7V1wRn2ygO1a3X9DoBEyU503kXKL0r92CIl5Q4wUVxRUxGTcy4j1dW82zm7WxBPXaEr1er6gZNikpWZAqRGpjWxtZ60C8r51z4N//JtfN0qX03hNPkE98wAA6x+GHx4u88Ofv30/CDABbt9L5jjuOArORCE1o6tkTGDPGKq3gVA7BT7ZQIiWMQ0A6JkxtADAawFMAvgzgz2lo05GqKheLXsFukp8uB173BKrLi082f17gdbakk7VoJ9Cqb1vObpHPWbu0VjvLVM2CWd24OiYAq1t+UOTg2yHnwcvXK/dZ/lwu+yAEV3ymWvMFrMAxx96OZC3xjFryOr+2XLDs1lvJsi8ooKDpxInkGhk6FPjzn2P/4IuKSPCnTAHefDO+rdJSsrBnzaLMmPZ2sryrq4Fly4ALLyTX0UUXkUjLmTZObhk/efd56t5Jh9D/EsCLjLHTAQwF8Jc0tOnIwoVkeceJ8tIG+im5bsT7f3seqCuMFWE/ue7qvunOn9fhNEjYuXx0JCtUdpOeZH+66u6x82uLY0WgU+6basW7TdJKlJRPbAoSOZvm738nsZdrv6xeTemJ3/42iThjlL3yz39S8PXee61zff/7wJlnkiX15puUPSOXLF640ArWyqK8cCH1YccOy6oSpQi8VtV0W+c2T+rO25Eyoeec13b9bGSMnQ2y6m/l3Mf6bSlEDU1EIuQ6VGdMC3btii9N4OfJoKoq/rXuWHU/N7yKSjKiI1vrbnVp7AKPuhmn6iQqHWIGrHpOu3o94n0vbiS1hIPI6LHrf1AlnrMCVfg++oiEddw4YPNmsvABEs577qEywxs20BOAEOGvf532GTcOeOEFEtt77iEL+667gEmT6HPhb3/9dRoMZFH+0Y+Aiy+2X3jEazkEt8JkeVJ33o601LrhnP8LwIp0tOWFOXNiDQ2AEgQqK7uEXlj2CqprZeHC2OwdgJ5eOY89vy4vXndsKvLnnXAaJNJhjcoWu9yWmnYo8vh1sQX5eDfEk4GaQx9E1otbho3bvmlHV5PmwAFaErC11XJtjBhBgrtpE7B+fay74/bbqfLkEUfQgiPCnw6QmJeVkbDfdRfw3/9tfS6L8qhRNOg0NcUK8I03WguKe3HLuBUmy5O683bkZVEzJ7dJdbWzlS4fKwQ/kawbu2MTzbrxmuuts6zdcKt+6UWw5CcCVVh1gVFdLRy7KpW6ujTitV29Ht2+uqcFNfvI6zKM6cD3gCH743U1aQDLn+llmT+xZuzy5cCHH8bmrqsF0kaPtvztsigPGUJWUVsbtdPWRgK8aVPswuNObhmvuLl3QkxeCr2T20Rnaav7yNjlwHsR7MfbajFgLrA9w4Khs+RlUdet3gQEVydGWNpCyEVJAjm9UnarqBa/3EddKQPV/aMuZ5gMXoPhfvdNCWogUtSC2b/fynkXQVcvy/w5+b0B4NlnrX8ktUCaGHSuu46qThYVWaWGVVdONErpmDfckFy9+DyoO29HXgq9k9tECPQ115BfXibdrpUgsKvKKFvWXgVHLVkgxNSrhav2RV7aT5c9I884dTu3XUVNtfSxLh5gV+NHvPaz+Lm4Lj+DiF/B9z1g2AnyqFFA794k5CKHXay3un9/7DJ/srUt3B1Ofm/OKZAr/snUQWLsWBp0vvMdei2eJDo7qa1IxDpu4EAqYQAkVy8+D+rO25GXQu/mNhFWelALgnz8MbDvkYbu85TOrKWEhmxYWUjBLtgpo8teSdS6Fy4ZedESddKTWKZQoJYr0KVOqqIul1kQr4O433YDqdd95dcpw06QH34Y+OtfY2u5AySAe/aQa4Nz2l8cJ9wdLS2UDjl7NnD55Zbf+/PPadGPVati8+j37QMOOojcJs89p59IBVDQtk8f8tn79aXneQkEJ/JS6AFvZQeSLU1QVwf86U9d8a4uV1FjIxDZ4n6s20xXv+iChG7nVHPgdbVpAMsV4td3LS884oSXpQqdUPttlyfvdF+8XJO6hKHfWjl+M6c8/004BSKPPNKq5S7WWf2f/wFOP5188KtXW5kF+/aRO2bKFMq02bSJBgvh977lFhowrr+ePtvS9YdeWEhPChUVsYOOOvO2uJj+Wfr3p9Wn/PrS8zRH3guRTHcgLNTV0aTASIR+Hj6nFtNfrUV7/65ZtjNqu/PzO59owL5HnBfyzhZk14cIkDbMiF94O5kqkOVF5d2VHIcdOgztt7Z3z7YVlR5190deYUrcQ3GcmioZRD+d0N0Tp319BVCDmMnptAC2+GzBAuuzfv1I8AcOJP+5IBKhOjciELt+PYnyTTdZ7pcbb6TgbGcn+Ts5Bx59lNqWZ96KEsXV1fTz2GOp/fJy/cLjdkyZQseIPk2fTq+nTEnunoWIvLXo3fDjttEVScO/nM+/YwcwQNNW0RW1GDgQeG+/fv3VIN08Xvzeaj35lgMtYPPiy8+KmvReZpQK94Z8TrlufLLYrQUrXrtVifQ6Z0B3nNwO4Oyv92SZr1yJ2q9sAh4didX7Nrvvb4ddILKlhcR63TqqO3PJJZRHf9xxwEsvAf/4R6ybRfwugqWFhWSBi6Dtvn30XjRKP+fOpQHkd7+jSVeANbBceinwi19Quy+8QMXNrryS/hnkksNuvvSgcuRD7PoxQq+hro7q1Ygn1sZGeg3oxV5XziBulq2Sm19VRf+o6iBx4ADw3nvoHgVaW2MNqnQh57S7rQlrh19BUgcKtRyCF2FMZgBMdD5BSmIscgD1EgDvbQaqkzifXSBy5UoS8/XrSXD79aOUyE2bgDvvtIRdLBjCGFWc/FeXJdPaGhu0Fa6hW2+lf5qSEuCxx6jgmUAedG6+mQad44+nbJwtW2gJQMC78AaVIx9i10/KqlcmSqL16IOsBNmnT3zGDUB/Q336xLcRiTiU1NYIfVkZ/e1PnepQSbPruLIVDXjsMUrFBNIXsFX9x6KapFxVUuSjt9/ars1KcQs6qnV0vNZlF085bnVx7K5FzR5Syz3ITzJyfXo71P6ouffiPL4CwGoVydJS1H6LAccfj4aZAVQR0VV8FH/EnZ3ONeJ1q0QddhgFYoVf/dxzqerk8uX0h758ub142lWfPPFEKqXgdKxg0iTglVfi20/0XuRg9cuk69FnOzrXiVquwA86kQcoEWHPnvg2HEshKJZ8ZSVw//1Wv9xq24jZuAPmxn9mZ0l6tTD9WKLyLFWBXekCtwlWySKCqkFNXFL7KweanVxm4j27AmvJ9qdBtVIHHG1VlEwWnbtD5M3v3Kmv5FdZaQVQOzvJ2i8poYW/77mHrG/hGrr0UvKTe6kto/aFc/rDX7vW/VhBMjnyeVAeIRRCH1QlSAC46irv+4o2Fi6kvy07GLN/yrAdJKQBYscOa1KV/OSC2RtRUuK9v36wK0cwump0XG15dbarOsFKV/Nd/uk1NVMVZFFt0uu12D1R6JbzU58ydOjKHwPxln3Cg5Eyk7Nh/fHerVQ3dO4O2QUjVo0SRCK05urpp1Np4h//2DruzjutujeAFcgdN46CqQBZyXbiqevLYYcB//mP5fN3E95kcuTzoDxCKITeqaSBH666KnalKa9tT52qn2AF6Bf5lq1Dt5m4gDUbt64OmP5qLTonbwSahgHFLdjHgSF31Mb8Xbul6/lJ61MzScSkJNk1IQue0yQoXU0ZXb0aJ1FU0zpbDrQkbdmrg5Iue8bp3ojr8pp14/l7Of0kYMxQNFyR5IxQO3QlATin96qrgXfeof2iUbJWRJrjhg3upQQGDyb3iRD61la9eAo//NFHk0+ztJRcQVVVVMo4COH14usPeXmEUAi9nVV88MHkA/fqt3/sscTaBsgdk0iRMnnyVmNj7CI76jnmzAE6zwBQtBs41BK891s24v94alIHddawTuDW7Fhju9i2m89e5LULK9qpL7VLa7szfBK9FoE6yUnOsVf9+E7n05VE1r32Tc+e1u+pmMmpc3eIinyrVlm+eM5JfJub7Y+TEUsJdkjfUWcn+dEvvjjW/SICoJMnA+ecY1lbTz2lF95EMmO8BFlDXh4hFMHYq66ip0r5UuyqSIogqA7dovVeqK72VszMLigoC4JdULl2aS1Wrwbl5APA/gqgpMutsL0GNTX+J9Ikmy0iH6+u16q7NjthFRSwAoyuGu2a4SIf5zvIadN/L9+Nl/N42c+tjYzPlNYEgjFwIAUsdVa1Kr7btlFtmg8/tP4pGaMYQEMDnUMNgMpEo1Z9m9/+lix7sQiJl+CuICRBVq9keinBlFJXB/zyl7Eizxi59dRSxMKnbneeRJEDs9u3k/GyfXtigeCpU+3PUVws7dg0DOgsAPZXoPo17xNwRH54UAgffQfvcLWy1YlCqvUvFxtzQvfUkMh1yf2RJ1z5ncSWzRPeHLGbjCVPaurRI77GjYpsMYvjFy+OtZwiEVo9Spxj1iw6b7TLqcBYbG7+oEGx9W3uvdf/pCg/SwyGHc55Vm0nn3wy90N1Neck8942xuLPsWwZ50VF/s6j2woK6Fxu1Pyihtf8osbXdYp+Ri6r4ZhRQ23OqOGRy2o8tZls227nw1xwzAWvWFTBKxZVeG6zYlEFL5hX0H28uq/dsV73S/R6Ukk62uCcc97czPnQofRTR10d/eEuXx7/2cSJnFdUcH733fRz0qT4fSZP5rxHD86jUTpPNEqvJ0+m4yMR6/3CwthziLYjETqmoMD6PRrlvL4+tq2tWzkfMoTz0lI6rrSUrm3bNud7UF9v9Ut33hABYD230dWct+j9BlyrquLLFVxzjX6tV4CMhspKb+fu6EjtAt9Tp1LMqriYjJ/q1xrwqzMbHGfsiuss+W4thtyR2ILVTvuqlrBclsDruZKpZSNX4gxiIW5xPU7nCfqpKGXnVi1tgZeSAbNmuZchsLOYv/iCFjBRLfrm5vi2OzvJsu/spH10JRoA/08ZAqfSD3lEzgu93dJ7lZXkk5cpKwPOP5/EuLGR7PDGRvu8eYDy5u+/3/pbdsPJPSTQPeqrg4/dYLH5pgbs/1mDq3tIzC0Q13nggFVjKpXI11a7tBa9F/eOEWE1JdFuYHAT8Fxzl8j9T3nfVTGdMoVETgi5k0tjxw6yJOTgb0kJncuLi+fzz+k9uUZOYSEJ8sMPx7ddUkLnWbKEyiA4DSx+RFu4pWbO9Fc3J6TkfNaNXW35+++n39XAprZcgQNVVfFljQ8+mIwWu6eAxkYSbK8zdIOe8CX6GnOdSxvQCaD4ylqceqq/IKOXNEyv1R3VypECNTCZ8tK9Njhds5/74Teg6reSpSPqBCCASgQL37SaN75/Pwl0nz5UkqC1lZb/e/BBSzA/+kiftSKnJYrqle+9F5+XftNNFAR9443Yz/bsoc+mT7fOaZdh5CczRjzNNDUBZ5zhfN48IOct+qlTKZOmurrLnVFtZdboApt+XD2FhVY2jXyuTz8FnnjCKr6nQzwteHHlOE34Arxb+zJ216mb8JgK1FmjolyCV5IJjmYTQbuWPDF4MFkjchngpibgy1+2rHrZOi4qIoHu3ZusboAmPDFG7330Eb1n5+I54wzgtttilyEUq0QJ6/vhhy03kmj7wgtpf9GmGyNGxBY7G65JMDGVLLWEIr3SD3a1ZYQfXrhx1FIFOnRpnTp0k6Zk7GrlMAb8v/8X/8TCGPDd7zr/f9hdp1tfdCRiXepq5ehcNF5TDQV2fUimj34tci+fqbNuvdbxCSy18vzzgZdfJmtElAQ+5hgrRXLdOuAnP6GqkroURzt69QLeeivWN65Lxzz0UODXv6Z/omefpTY6OijLRtTJiUSo3YICcuEEkfboNzU0RIQ6vdIvCxfqfff330+Wusih+fRTZ5HXpXXa4fYUYRdnqKrSW/uc0wDjZNnbXWe6lkJULXJRU35j00as2bHGc9mDbLbkvVjnIgaR9ieTefOoBjzn1h+CHLwcMYLcM7K/PKp4cg8+OL506sKF8YKp89XfeSdwyin0/hFHWJOnolFKnRw40Gq3qCi4tMdEg7Zhxy4dJ1Ob3/TKRFi2jNIyGaOfftITBX7TOp3aWbaM87Ky2P3Lyuh9xpzPmerr9IqfNMiKRRXd73V/bpMKqKZvBpGaGNQ5nY5LVfqnL7ykSMrph+IPa9w4+llZaf0BRiL0+/jxdJz6fenaEvtceWVsfnN9fWrTHsePp/4uWGB/3SEEYU6v9ILq4waSn9jkN63TyV/vFGews/a99MFp8lW6kNMVRQaOCMiKmjkA7FMBEyQdKZB+/O6qJZ9yX728oIjXTJaSEipD8PzzwLvvkuVdVkb+9+JikmrhF1e/r1mzqK3HH6eg7syZ1gLgjz5qtcc5lUK4/vrUpT2edBL90R95ZF5n2sRgNwJkagvaol+2jOZqyJZwYWHy1q1fi96rFa7rf5xVP6Ome9LUzJnJXUeyeLWOhSUv9hOTq2puPMR+0o2mHb/9SnYfp+OSeSJIuYUvJiQtWeI8aWrtWs6bmuj3pibO162L/Wz8eGtCk7DI5Z/y9yXaBGgGovhOdbMWR4/mfMsW6tuWLbHtyrhN+pJxmsCVB8DBos+4sKtb0EJfWan/W6us9Hce1Q0yc2b8AJLozFw3Zs5UxF4S+kyLvV/RU103Xmc8ehVGdUBxdK88PorXXFXmTURs2kpU4D0PEn6EjvN4sRMCPXKkr352o34/xcUk4iUl1vfVq5c1C9bLdthh9P06zcwVeNnHrq9eZ8+GBCehD73rxm4ylNMkKRV18lFjIwVD1Vo6MnazaTm3UiS9pk0+/DBl33QvMD4gdsHxRKpuBoWnNEinBa49Bs9SEcRsKLsKDQ/vDcxdlBL8urTEhCTelSUggqBr1yaWZqh+Px0dwPe/T9ky4vu6/fb4yL9AVynw888p1dMpBTKRNEkTiLXHbgTI1Ba0Re9kWLgxc6ZlECWyOQVSCwvjn2yLipxdSt2WvHB/SJa9H2ytSL/Wo5dzcu5ulYlA3oIFZBmKgJ/bedU+dFnoMa4h3bFZ8Ijvel3J9FEEOuU/wNJSzo85hvPBg/1/v2qgtX//+MDrAw/o/wEuvdR6LVv9jFmvdZZ3ota5lwB0SIFx3cRvqutGdc2cdVbiAp/MFonQAKPLlukedBTXTUGBv3sSIzKyuLsIsm9XhVexEn5i0f7tt1tt+nCv1NwxhNfMgLvQZ8Ejvuu9TKaPQuymT7f+QKJRzq++2nnAtUP14y9dGu/Xnzgx3ioqKaHv7ec/57yqKtb9078/7e+UdZNIZo5TzCHk5LXQ6ypTqpazLr0xmzaRajlzpl7ovfrotf7hO4ZY/3wBB0Q9i5VmQKi5LMJrbjzE6usdQ+zbVo6v+RZ4zWURZ+s3F6oaJtpHIXYTJ3LesyfnRx/NYx4xvT4d+HnCO+cc/SOsiA2o1zJypLvlncfWeSKEXujd8sXdPk80gyadmyiBLLuTCgr8BWLjhP6yCK/5lqax0lL+2eFD+emHbeOMcV58ZQ0/dnFNYlkmXsRKMyDUXF7Aay6LWG06ibdyfM1lEXoKcLJ+FRGpmdW3+7rSnu9uR7JCJwR/61bOjzqKBnN1wHUSc7+B0AED4sVefPdHHBF7LWPGuFveeWydJ0Kohd5pspFXnHzp2bT5vS75HskD3bGLa3jNw1+JFVfx2FNayjsKonxKUb3Vdlfd+4SE3qtYqQPCAw9wPmQIifYMuLsu/Fq/iojUPHCSd6FPIpbhiyCFzu7+6MQ80fhAfT1ZH2psYOhQzp9+2oh2igm10NtZ437y1f1Y9HapwenaEsnDVwfCyGVkocf88zNGO959N29hFfw3mBTXdvGVCYigV7HSDQj19ZYl7ybeCVq/6lOOp8HMj6WbCXQDkXp/qqrsxTzZQOi0adY5s9UtFkJCLfR21rjXfPVly/QB27IyCsjKbpLycm9inMonBL95+LpBrBea+fuFQym7Rfzz9+jB+QUXcM4574cmfjLWxbc/w4PQJyqCugHBj3gnaP36EvosyNbxhO47UO/P0087i3kygVARG7jmmvhyCKl+CspjQi30yVj0y5bpLfTKSr2LxKuAM6aZ5ORxc0vn9GvR6/owGSQEc7CAn9y/ia5VEseE7mkqRNCneNtmE3lAdtvY1t7JgmwdR/x+B05inkx8QPe9ZftTUAgItdAn46O3s9DLy+Pb8OPeqa5OTYBXvi6vBcvkftRhMv8CPXgrSAhaEeVfoAf/TcFk1ywkuW2ttZsFIhjTr7o6XjODsnX8HNt9DjthyuZsHb/fgZOYBxUfyJWnoBAQaqHnPPEqjU6iKp/bT+qll6qTiWzywuN+Bjd53yOxlf8dQ/hukBDsRin/G4byQdgWZ6073VNb/3WGRNAum6hmBmXr+BIWIUyyz04+PttT/vx8B+nIaskCAyBfyKjQg5Yr3AGgoWs7wWn/dJQpFngReifLXNS80QlikBa9mvfv17Uii/YE1PNWRPnnXZb9BFB2jRffv2udlgyJoNqvipsjvGK25G+/qoyyjLwghEkUMiosjBWmbE/5y8aBKJufgkKEk9Cno9bNiQB+zTmv7dreTUObntCV4RCI+jO6VZrEsdu3Ux0aXSlg3cIfiULjpYVdeWK79+VyxTPKVmAPemAu5mEPemAiqDysUzlkz8yalZGFmNV6O8N6H4thH0s7DBhAKw154dZbgQ8/tAoZtbUBH3xA66EC3pazyyQZ+g4cEaWQZ8+mP0JdUSenekiG5LEbAYLaAFwF4O8A1gL4OYCo0/5BWvRuLp3umaYJbCJd2G1BEbuMHrvSDE5bZaXzcV4CtS/OW8sHlDZxgPNDurJr/ObnZ9WkIonufnVZtTXzB/Gaywv0Vq1dsHbrVsoYkW9sr17G1ZAMDiUuujHB2qRBhl03IwB8qev3XwG4QLPPFQDWA1hfVVUVyEXrSh9Eo9aiOUKgky1cJtweQmi9zrqNRBIrc2y3+RHrZFeeylah76ZLWGp+UcNrHh2pd684CcuiRbGuhsWLg+tbPqYZOgVkTbA2MDIt9MXS71cDuMFp/6Asei8WsyqOqRBcp6BsUVFilr3Okvc9YzZXBCfofroJS3MzWfS9eqXGz50uyzWbvt+tW6nejvhnkAOyJlgbGJkW+hUAvgygAMCrAMY47R+U0PtxhwjrNijrWnahuAVlKyuTs+wTWciEc87544/TCZYscd4vDYKR1lmobsIi2nvkEXodVMA1WcvV7/eQba4QUTlTFM+TA7ImWBsImRb64wG8A+BdAAvd9k+30Kdik8U31ZUx/U6g6hYcUQs8EnEWHJ1gBCz+cULf3EwWdVmZf2F06VvNL2p4zZ1D44VFJ8SMcT5hQiDXmLTl6lW4s80VIvojLzIOcH744db3lI2ZQjlIRoXe75ZO1026xHfZsuTjALotoSJn48bFP74wRu9zbgnlhAn2ghGQtWibrinOf9hhzsKoE3UvNfVn9Y0XFlWIRYDnvvuSusYYErFcvQi3fB+yzRUi+iNXzjz6aM6vu876nrI9ZTVHyEuh1y0Kno5NXnhcDnr26JHY+dRMG6fAr+4exAVdt24la0pupH//eNfFvffGC0avXvQzIGsxTuhvPMQqYCZbf3bFsWRRdxHEuLYeHUmDiiws9fX2ZXaDqN2eiOXqRbjVwS3VrhC/T3Rq8TwPax8Y/BN6obfLInGypAsKUmP1i/IJQblsZs5MrMSD4+zZkSN5jNU6cqReKIuLrVFKKh0ctLXYbcmrohaJ0DZ7dqww6voqD0SavrlO9uKchLi8nPNDDrFumtdr9PKUo1qur73mTTDthNtucFNrvwftCvH7RCcPcOXlFOzOlieOEBFqoXcrB+AkoqnynzMWjKtGzLrVfSYHkf2kdVZXcyrL2bMn/eP17EmLQGgsxwMlPfkXrJzfACpd3HjyeP0ScEn67GNEVxa1ggIKGnMea3nbWbkPPuhqyToGfoUQi7rqJSXuFnEyPnFZMJ3uod2TwFtv0WBdUhJ7H1JV+z3Ra1UHuMWLTfA1BYRa6J2EkHNni57zguC7NAAAFSNJREFU2KeBVPjRE9kiEUu4/VbMFDiWb7bziUoi21EQ5T+OLuKHwJpcNTe6wBIUeSHvIDM8vLg3mptjBxyA81/+0tOxnipc+nGx+PWJ2wWahQ9bdw/tvi9x34V1k2rRVK+VMVpw3K81boKvKSHUQu8khG4zX1USLS0c9CYvXO6nZg5j7rV2HLN0pH9AefERtepljDAVFqa/NLEQuNJSzqdMod9POcV7UE8I/P3307Ff+lKs2PsNDqqulV/+Mn4AEW2KtFY50CxvXu6halkDNOgVFqZeNMW1iu//mmv8n8MEX1NCqIXeSQidRFvNP9e5cRhzLmWcqmBvMumZosplQuWbpX9AefERteql7eZ1LdJEmTzZ/maoVSad2hcxCvkPpLg4Xly9XoNqoZ5yCp1Tts5Fm3JVTPmn7h7qaG7mfOBA/T0oKeH83HOd+5rs93LEEdSWCJSLGI4JpmacUAv9smWJC6qM3YChGyzEe15XnPK7uZUMdgsiC0FPptSBej9E1cs9rEtoRSBX/FRdB27unEQEZ+tWsoTlL0J8GaobwWktVLsbF1EWH3e6Brn/YoAUA5EQ72iUfhebrr3Zs617qJtMpLYnngjUragouKCxE//7v5wPGmTd9+JiE0zNEkIt9Jwnnj0jkw0uG3mzW+WKc2/17n1PpNK0IRvPT2Iib0YFXz/lbvpAWHLSWrO8Vy8SKi8TnfwKjpM1L7sRJkyIDbjo1kIVwUt1GzyYBEvnGikrcx8EdP76wYM5P/LIWDeN6N+IEbEDj5xOqg6A4onAbmPM2T8v7ksQbjanWa6GjBF6oXcSPrv3ZT8456lZEcrrJv6/1U2tQy9fr1tefsKlEaQ25AF0TMVa/tRDXX7Vr32NOqCsNcsffph2PvRQfV0TzhPP3FCteVmEZTeCXEde177wMetuuhAsVbABmnugGwTU/utSIcV7YvCZPp1cPGPHxk8mOvRQHjOAqAuhOP2ROxUEvOce649Kd1+8YDfLVbSbTfV18pDQCz3n+kBqUZH+/zkajRfQVJcq0G1lZe7VM3VuHC/9VAcyP7j699Vg2rnnxoqf7E5RLb5EZm7aid2RR5LPWBfUlEVQTgEVC6IPHmztI1wrciBz1Kj48/ToQTOInfqvyygR7119NaWzTppkBSHFIKCbJCbaO+YY50c4Mdv0t7+1v3fq4shuTwA67Ga5inazrb5OnpEXQs+5d1+2EEF1/2Tq0/vdKiv1k6F0m4zXJ49khN53xo4QAHVUVS0+gdvMTdUyFOdXhf7ss61zqaULAM779aNAyqRJlggtWEBiX1ISK6rFxZyfc47Vh/POsyZsCWEcOpTzt9+m1E67/q9dy/mWLbTvhg0UOH31VfssEzEIzJ4d2548gBx9tP0XbTdrWP1u5MFQvi9+0X132VZfJ0/JG6FXcXLn2Fmt6aiRI0TYi2jLKZNO12QnzIkEYh1z8HXIee3ywXaWplsetc4yVC1sgNwzhx9O4rhggRU7EGUa6ustUfcxi5ZzTqKvLmhQXGz5yktL7fsvAqbf+U78dajIT0ePPx47GW3UqFhXiW47+mj3XHQhziUldP76+sTTGnXfXbbV18lT8lLoncofVFc7T7RKtdALwfQq2vIs2EQndfkpgObbopfz2qdNswTVztK0y6N2Chied56+U2Lf5ctjYwduIuQ2i1a30hQQv2j4+PFW/+2yerymIKoiKnz4dkEcgFw7bqId5AQlD5PtTIA2M+Sd0Dv5sbMhu6ZHj9RVtHTaYoTaIXDmOQdffWQXvu5olDJg/IqKU8Bw7VrOFy60vzhRsmD8eDqXmwh5ET95pamCAqqB42S16iqDAiTUXixcnYiKvuuu+dBDvVnN6ZigZGa7Zpy8E/pMZtB43ez+d71u8pq1XpdDjHG9uATOPOXg66zlgQM5//Of6XOvouI1YDhxIo04soUrX/gxx5APXR3AdCLkRfzU40aOdH8KUCuD6gLSfhB9OP/8+C905MjYfTOZ9WJmu2acvBP6bLDaE9nk7EAvm99lEKurueUeEWKpzii1w05Egnhk9xowXLuW3DMVFZwff3z8BUajlg9dHsASFSH1uLPO0lut8r059VTryxQ3PRkLV/Rh4kSrzIGIOYwZE9u+iA2YrJe8JO+EPhcsertNVKz0MgtWdqe4WfTd+wr3iDigsNDKJnGyBu2eAIJ6ZNcFDLdsoScEXR2arVudH4vE4DNhAln6xxzjzdJ1sopF283NFAR97TX6XVjxy5dblUGvvpraHzMmGAt37VrOX3qJztXUxPmqVdY51fIKYtJar14mpz2PyDuhz0ROfFCbcJMIsXdbU1b43d3O+eHIyfYZHHI2yeGHx4qDW+pcUI/sugHDLS/72GPtL1r40O+913rPi6Ur2lyyxF7wxT4jR1o55eLe2MUJ7FAHFj/uF6fJVF/6kvdrNoSC0Au9zp+siqVT4kK2b2qWn7wJv7trpow62UXdVLEQa6WmKnVOFTR5wNClRKrupcmT7b9UkWZp97luHVh1QBPHyn5wu1iCvPXq5e/eqIOZn0lHOpeX/Idhd+8MoSTUQu+UISLE3u5/MtHl/TKxOblmxGeqsR6XKaNOxS8spN914l9U5DytP1mcBM3L4PLWW3rBjUSslYzsnmB068DaTfrS1cqR67Gr537wQW/Xrw4afpYwlAdJNStHPY/XjB9DzhNqoXfKh3dz34jFOjIl0H43J8te/T/XZsqIErPq1ru3/v3SUhKaiRPJUu3Xj34mkzrndRal2+AiL7ohBK2oiPPTT6fPxUpGui/AqU3dpC9drZwePahNue5LWZn3e6MOGiI2Ydeu7tqXL7dcXtOnUx9017t0qbc+GXKaUAt9Mhk2wq2hFvAKarObgZvoVljorZ+2E5v+9385P+ooy4IvLqag4mmnxVd0lMverl1rFSx75JF4v7Mfv7JXV5BdkNdp0Y0LLogvLxCN0rU4ibe8f69e1J4YENRBRu6XeDq67TZ6T23fDfUJS32aGDUqdn/dICnHBMTMWnG+0aPp56mneu+TIWcJtdAnmmEjCooJP34qfPjyQCLHEM46K36A8jpgifO4DTC26CxlkbYo56iLHHYvFrgXv3Jzs5X9snSpuyvILsi7dSuJsXrR6qIbYoGOp56i44UIyuURVF591aoeWVKin/Ql9+ullyj7Re2jE/KgqNa5kUf0SIRy52XcBklxPlEbR/yheE2hNeQ0oRZ6O9eL06IgYpJRKjNz1IFEdaXoCqp56Y/Y38sAo8XOUpZz1G+7jYTBrY6Jn2JWsqvllFO8pWTaPSk88EDsBYu0pOpqa1918HFLA508OTZWoStxEARyVs/AgZRCyjkNRML9E43Sa127Ti4tOfVUfnIztWfyglALvR8fvRyczFSuvVhQxClTyE3EndxBfpYLjLNC/dYxcbMwxULYOr9xQQGJq5Ml7JS7r86QFdvIkfrB55xzrGtT8/MnT9ZnrvjJoHFzX+nKRYj+imvyMvip+40fn7qJbIacItRC71ah0s6i9uoqEQIcdFBVzY33MgjJlSzlQUH0zW+VSltU0Ro/3qoQqYqQk6AIoT7kkPgLEas56fCSu/+1rzl/ieKm6KxZdQARA5b6pXjNoNGdU8Wu1LJ4cjjjDHK5NDfHDn5OaahNTfSd2A2GpvZMXhFqofddadHlOJ31nS7rX1SpFFqoauPMmc7XlMwasd2oMz0553z+fOu1XT11WVC85JurgUYZLwHbtWs5X7FCP5usTx8re0bMsuXceQCpr4+92V4zaPy4r8SgqAsMi4ldqmDbDSBO7Yr4hHALmdozeUGohd4uj97JP253XLZtjqmSHu+DL7FX/dTySlF2IiZbmMIl8tZb9hN5xDZ2rHUOndvDq+vh+uvjz11aamURlZZagm03gIwbp7e05eCuHV4GJXV1q+nTrTYYs9Zele9xdbXzAOLUrlnpKS8JtdBz7i2wqRM9dfas3xRLMaCksoia10W+nWIVnrDzU0ci3oN6ssDo8s1FoHHOnFgLUydM4klhwQI6XqQQqvTvbwmh2m/xfkmJ8+SvrVs5HzDAGhxKSmjA8uqf95rzv2CBVaCsZ0/K6ikvp99Vwf79790HELVdsVCJWekpLwm90Ksk6s7h3LvYyzVpEhFwP+mcskVv555xGmwqKz24c4SFqM7Kuuwyd8ta50YQue0i3zwadc+Jl4VJPCkIkbz9dn2/ly6lDJaePUmg3WaG2vmukwle6s4pAtFicJOvTw4Mi4ldurbd+iS326sXDRpHH21WespT8k7ofS+Fx/2JtjxgJGrNy6tGuVWpBKynB7snFa99d3Tn1NfHz/Ts3989qOdWl94u3zzo1M3DDot9KlF99JzbZxYlE7zUnVPXJ7+Tw9z6JLcrJrRdc43JtslT8k7o/Vr0fvz1slAmm42jWupufXBaGnHZMn/tapk4kQRCnum5dKm36pSJWsSJpm5ybl+ITAxW06Z5F+2gqnDapVGKlbf8TA7z0ie7mjnDhplsmzwj74Teb2DSj/tFZL4sW+at9ozfgSMRV5B4UvHqdrJ9sklG7BK1iFX3g1xD3W3wUAeDSIS2Sy8lV86kSenPOFH7VFBAfZo9OzXCq7Ynylps2+Z87ZlcjcqQEvJO6Dl3L13s1b9tZw0HWRtHXhbQSeydLHpxzV6eTLwGeH3hd5AQQvPqq/HuB6+zWTmPHQwKCmhGqdc+pIp09qm5mdxrfp+mTGZO6MhLoVdxsvL9WNHCGg5K5HWb7kmhqEhfTVhNJRXZQ3aLlvhOuUwVstDY+ePVoOW6dfGWaDZODEpnn8R9LC311p6f2Ichp8g7oddZ7k5+ez8+ei8rOqVzGzrU2U0VyCSqINEJTWkpuWy8ZIuolmg2Lkqdjj7pYgFeavOkaiEZQ8bJK6G3s9zdLHSvgVUhlKkoaxzklhL3TBDYCc2DDzq7H4wlGksygm3q4ISSvBF6J7F2829z7u6rlycf+clyycTmWKrYjVQH6nRC4+buCNISDfr6MhXYTFSws9HdZUiavBB6L+4Xt6X23Hz16sSjbLbqk7Lokw3UuQmfTmi8uDuCskTtFgBPVLAzFdhMVLCz0d1lSJq8EPpERLe8PLZsglMN+1zdRGE2TwTlHnETvkSFxqmKpoos2uL3CRP0efdf+Qp9/vjj/gQ70+6kdAm2ScXMCbJC6AH8HMCbAH7stF+itW4yLajZvBUVeRT7ZN0jqRY+pyqaKvJgI36/9179AuDq5rXf+RLYNKmYOUHGhR7ARQCWdv3+BICj7PZNROgztYhILm2eXTnJuEdSJXx+BhCnEsnRqJWj6hSQUevjOBHmwGamn1gMvnAS+gjSQy2AFV2/vwJgtPwhY+wKxth6xtj6Tz75xPfJd+xIun+hx/M9WrEC6NEDmDePftbXe29k8GBg/nygrY2ObWuj8xx5ZEJ97mb+fKCqCigspNeFhUB1NbBggfu+jNEmjisuBsrLgUsu0bcl9vfa72TuV7bj574bshu7ESDIDeS2+XLX7+cAmG23r7HoM2zRJ+v3TVVGhx/LWS2RLHLMo1HOFy2KLRV8/vmxN8pPfRzOwx/YDPMTS8hAFlj0uwGUdv1eDgTb7sKFQFlZkGcMF0VFdI88MWIE0K8f/d6vHzB8uL/GZs0C3n8fuOEG+jlrlr/j7fBjOcv7RiK0iePefpuua9YsYOtWeq9nT+Dii+nngQP++p3s/cp2wvzEkk/YjQBBbgCmA/hh1+/zAEyx2zeZ9Eq7MgAiI0deW1VdgWrmTP+ZO0EvOFJZyflZZznHCnv0sNbHkDdxbZWVtI98zozPhg0CP5azvK9dieREzpuPmPuTM8DBomf0eWphjPUC8DqAPwAYC+BUznmLbt/hw4fz9evXp7xPBoPBECYYYxs459pHyrS4bjjnn4MCsn8GcIadyBsMBoMheKLpaohz/hmszBuDwWAwpIl0BWMNBoPBkCGM0BsMBkPIMUJvMBgMIccIvcFgMISctKRX+oEx9gmAxgQP7wPg0wC7k8uYe0GY+2Bh7gUR1vtQzTnvq/sg64Q+GRhj6+3ySPMNcy8Icx8szL0g8vE+GNeNwWAwhBwj9AaDwRBywib0j2W6A1mEuReEuQ8W5l4QeXcfQuWjNxgMBkM8YbPoDQaDwaBghN5gMBhCTmiEnjH2c8bYm4yxH2e6L6mAMVbBGHuJMfYKY+y3jLEi3TUn814uwRjrxxh7u+v3vL0PAMAYe5gx9rWu3/PuXjDGDmKMvdi1HOmjXe/l3X1wIhRCzxi7CEAB53wkgEGMsaMy3acUMBXAvZzzcwA0AbgYyjXr7oPX9zJ0TclwN4DSZK45DPeBMXY6gEM558/n8b2YBqCuKze+J2PsRuTnfbAlbWWKU0wt4hcf35qx3qQAzvnD0su+AC4B8NOu1+Ka/wvx98HrezlzvxhjZwLYAxrwapH4Nef6fSgE8DiAFxljFyJ/78UuAMczxnoDOAJAC/LzPtgSCoseQA8AH3X9/h8A/TLYl5TCGBsJ4CAA/0T8Nevug9f3cgLGWBGAWwDM7normWvO2fvQxXQAmwDcCeArAL6H/LwXawBUA7gawGYARcjP+2BLWIQ+pYuPZwuMsYMBPAjgMuivOZn3coXZAB7mnDd3vc7X+wCQ9fkY57wJwDIAf0R+3ovbAHyXcz4fwHsApiA/74MtobgIABtAj1gA8GUA2zPXldTQZcnWA/gR57wR+mtO5r1cYQyA7zHGGgAMA/A15Od9AIBtAAZ1/T4cwADk5704CMAJjLECAKcAWIz8vA+2hMVH/wyA1xljh6Fr8fEM9ycVXA7gJABzGGNzAPwCwDTlmjni74PX93ICzvl/i9+7xP4CJH7NOXsfuvg5gCcYYxcDKAT56J/Lw3uxCPT/UA3gTQD3IX//JrSEZmYsY+wgAGcD+GPXo2zo0V1zMu/lKuY+WJh7QZj7EEtohN5gMBgMesLiozcYDAaDDUboDQaDIeQYoTcYDIaQY4TeYDAYQo4ReoPBYAg5RugNBhcYY6MZY/WMsUhXVcMjMt0ng8EPJr3SYPAAY+wXAPYBaOSc35Hp/hgMfjBCbzB4gDE2HDTrsq9UZ8dgyAmM0BsMHmCMPQPgHQA9OOc3ZLo/BoMfjI/eYHCBMfYNAP/inN8K4DjG2EmZ7pPB4Adj0RsMBkPIMRa9wWAwhBwj9AaDwRByjNAbDAZDyDFCbzAYDCHHCL3BYDCEHCP0BoPBEHL+P08u5F0zuAQFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_KNN(returnMat,label,0,1)\n",
    "plot_KNN(returnMat,label,0,2)\n",
    "plot_KNN(returnMat,label,1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#数据归一化\n",
    "def autoNorm(dataset):\n",
    "    minval=dataset.min(0)\n",
    "    maxval=dataset.max(0)\n",
    "    newval=(dataset-minval)/(maxval-minval)\n",
    "    return newval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datingClassTest(filename,test_ratio=0.1,k=3):  #测试集比例为10%\n",
    "    datingDataMat,datingLabels=file2matrix(filename)\n",
    "    normMat=autoNorm(datingDataMat)\n",
    "    m=normMat.shape[0]\n",
    "    test_num=int(test_ratio*m)\n",
    "    count=0.0\n",
    "    for i in range(test_num): \n",
    "        classify_ans=classify0(normMat[i,:],normMat[test_num:m,:],\n",
    "                               datingLabels[test_num:m],k)\n",
    "        print('the classifier came back with {:},real:{:}'.format(classify_ans,datingLabels[i]))  #对测试集分类\n",
    "        if classify_ans!=datingLabels[i]:    #对比分类正确性，记录错误次数\n",
    "            count+=1.0\n",
    "    print('error:{:.3f}'.format(count/test_num)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 3,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:3\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 3,real:3\n",
      "the classifier came back with 2,real:2\n",
      "the classifier came back with 1,real:1\n",
      "the classifier came back with 3,real:1\n",
      "error:0.050\n"
     ]
    }
   ],
   "source": [
    "datingClassTest('data/datingTestSet2.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img2vector(filename): #处理单个文件，转化为一行\n",
    "    vector=np.zeros((1,1024))\n",
    "    fr=open(filename)\n",
    "    for i in range(32):  #每个数据都是32*32\n",
    "        line=fr.readline() #读一行\n",
    "        for j in range(32):#存下这一行\n",
    "            vector[0,32*i+j]=int(line[j]) \n",
    "    return vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handwritingClassTest(trainpath=r'yourpath',testpath=r'yourpath'): #将yourpath 改为自己存放数据的路径\n",
    "    hwLabels=[]\n",
    "    trainingFileList=os.listdir(trainpath) # 返回[....]\n",
    "    m=len(trainingFileList)              \n",
    "    trainMat=np.zeros((m,1024))     #总的training实例数据数量\n",
    "    for i in range(m):              #处理label\n",
    "        fileNameStr=trainingFileList[i]        \n",
    "        label=int(fileNameStr.split('_')[0]) \n",
    "        hwLabels.append(label)\n",
    "        trainMat[i,:]=img2vector('{}/{:s}'.format(trainpath,fileNameStr))\n",
    "    testFileList=os.listdir(testpath)\n",
    "    error=0.0\n",
    "    mTest=len(testFileList)\n",
    "    for i in range(mTest):\n",
    "        fileNameStr=testFileList[i]\n",
    "        classNumStr=int(fileNameStr.split('_')[0])\n",
    "        vectorUnderTest=img2vector('{}/{:s}'.format(testpath,fileNameStr))\n",
    "        classifierResult=classify0(vectorUnderTest,trainMat,hwLabels,3)\n",
    "        print('the pre:%d,  the real:%d'%(classifierResult,classNumStr))\n",
    "        if classifierResult!=classNumStr:\n",
    "            error+=1\n",
    "    print('error rate:%f'%(error/mTest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](pic/os.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the pre:0,  the real:0\n",
      "the pre:0,  the real:0\n",
      "the pre:0,  the real:0\n",
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      "the pre:0,  the real:0\n",
      "the pre:0,  the real:0\n",
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      "the pre:4,  the real:4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the pre:4,  the real:4\n",
      "the pre:4,  the real:4\n",
      "the pre:4,  the real:4\n",
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      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:7,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "the pre:9,  the real:9\n",
      "error rate:0.010571\n"
     ]
    }
   ],
   "source": [
    "handwritingClassTest('data/trainingDigits/','data/testDigits/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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